Estimation and control of contaminant transport in water reservoirs

ESTIMATION AND CONTROL OF CONTAMINANT TRANSPORT IN WATER RESERVOIRS NGUYEN NGOC HIEN (B. Eng (Hons), Ho Chi Minh City University of Technology) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN COMPUTATIONAL ENGINEERING ( CE ) SINGAPORE - MIT ALLIANCE NATIONAL UNIVERSITY OF SINGAPORE 2012 Acknowledgments It is time for me to express my deepest gratitude to my supervisors, my friends and my family and wife for all the things they have done to support me to finish this dissertation. First and foremost, my utmost gratitude to Prof. Karen Willcox whose encouragement, guidance, understanding and support I will never forget. Her wisdom, knowledge and commitment to the highest standards inspired and motivated me. Her excellent guidance, discussions and patience helped me in all the time of research and writing. Her supports in both the academic life and real life provided me an excellent atmosphere for studying. It is very lucky for me to have a very nice advisor. I could not have imagined having a better advisor for my Ph.D. time. Second, I am heartily thankful to Prof. Khoo Boo Cheong, who let me experience the very interested research of reservoir’s water problems, constantly and patiently guided and corrected my writing and financially supported my research. I would also like to express my appreciation and thank for his kind helps and support for the past several years since I applied for Ph.D. candidate at Singapore-MIT Alliance program. I am grateful to Prof. Nguyen Thien Tong for teaching me with a strong background and then supporting and encouraging me to follow the professional academic path and to pursue this degree. My life has turned to a new page with many chances to fulfil my long cherished dream. I am really appreciated for what he has done for me. iii My sincere thanks go to Dr. Michalis Frangos, a post-doc, for discussion, guidance and support during the time that I studied at MIT. I would like to thank Dr. Galelli Stefano for insight discussion and suggestion for control topic. Special thanks go to Dr. Huynh Dinh Bao Phuong, who as a brother, has been willing to share my real life problems with his warm support. Many thanks to Dr. Hoang Khac Chi, who is a good friend, for very interesting and helpful discussions. It would have been a lonely lab without him. I would like to thank to all my friends, Dr. Le Hong Hieu, Dr. Nguyen Hoang Huy, Dr. Huynh Le Ngoc Thanh, and Dr. Nguyen Van Bo for their assistance, support, encouragement, and warm. I also thank my fellow labmates in SMA, NUS and MIT (ACDL) for the exciting discussions and for all the fun we have had in the past several years. I would also like to thank to all staff members at SMA office and specially Michael, Belmond, Nora, Juliana, Lyn, Nurdiana for very kind helps. I also wish to acknowledge the opportunity that Singapore-MIT Alliance has given to me, not only supporting me financially but also providing me the best study environment. Last but not the least, I would like to give all love and thank to my family members: my parents, two elder brothers, three elder sisters, my wife and daughter. To my parents, Nguyen Van Hich and Nguyen Thi Ba, for their love and support throughout my life. To my brothers and sisters, they are always supporting and encouraging me with their best wishes. Especially to my wife, Dong Thi Lan Anh, without her love, patient and encouragement, I would not have finished the dissertation. To my daughter, she is my love, my life, and my iv motivations to move forward for not only this degree but also for all the future targets. v Contents Page Thesis summary x List of Tables xi List of Figures xiii List of Symbols xvii Introduction 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2.1 State of the art in reservoir simulations . . . . . . . . . 1.2.2 Inverse problems . . . . . . . . . . . . . . . . . . . . . 1.2.3 Optimal control for reservoir problems . . . . . . . . . 1.2.4 Model order reduction for reservoir management applications . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Thesis Objectives and Outline . . . . . . . . . . . . . . . . . . 10 Mathematical model and Numerical methods vi 12 2.1 Laterally averaged model for lakes and reservoirs . . . . . . . . 13 2.2 Transport and thermal properties . . . . . . . . . . . . . . . . . 17 2.3 2.4 2.2.1 Water temperature . . . . . . . . . . . . . . . . . . . . 17 2.2.2 Water density . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.3 Dynamic viscosity . . . . . . . . . . . . . . . . . . . . 19 Boundary conditions . . . . . . . . . . . . . . . . . . . . . . . 19 2.3.1 Boundary conditions for fluid flow . . . . . . . . . . . . 20 2.3.2 Boundary conditions for water temperature . . . . . . . 23 2.3.3 Boundary conditions for contaminant transport . . . . . 24 Numerical methods for lateral reservoir system . . . . . . . . . 25 2.4.1 Turbulent models . . . . . . . . . . . . . . . . . . . . . 25 2.4.2 Numerical model for Navier-Stokes equations . . . . . . 26 2.4.3 Numerical model for transport equations . . . . . . . . 31 Code Verification and Validation on Benchmark Problems 35 3.1 Cavity flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 3.2 Backward facing step flows . . . . . . . . . . . . . . . . . . . . 37 3.3 Validation of code for transport equation . . . . . . . . . . . . . 40 3.4 3.3.1 Pure diffusion equation . . . . . . . . . . . . . . . . . . 40 3.3.2 Convection-diffusion equation . . . . . . . . . . . . . . 42 Numerical simulations for 2D hydrodynamic processes . . . . . 44 3.4.1 Model set up . . . . . . . . . . . . . . . . . . . . . . . 44 3.4.2 Velocity field and pressure field . . . . . . . . . . . . . 45 3.4.3 Temperature field . . . . . . . . . . . . . . . . . . . . . 48 3.4.4 Contaminant field . . . . . . . . . . . . . . . . . . . . . 51 vii Reduced-Order Modeling 4.1 4.2 53 General reduction framework for linear system . . . . . . . . . 53 4.1.1 Reduction via Projection . . . . . . . . . . . . . . . . . 54 4.1.2 Proper Orthogonal Decomposition . . . . . . . . . . . . 56 4.1.3 Error quantification . . . . . . . . . . . . . . . . . . . . 57 Reduced order model for non-linear systems . . . . . . . . . . . 57 4.2.1 Galerkin projection method . . . . . . . . . . . . . . . 58 4.2.2 Galerkin system . . . . . . . . . . . . . . . . . . . . . 60 4.2.3 Numerical example for ROM of non-linear system . . . 62 Optimal control for contaminant transport 5.1 5.2 5.3 66 Deterministic control for contaminant transport . . . . . . . . . 67 5.1.1 Formulation . . . . . . . . . . . . . . . . . . . . . . . . 67 5.1.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.1.3 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . 75 Stochastic control for contaminant transport . . . . . . . . . . . 75 5.2.1 Formulation . . . . . . . . . . . . . . . . . . . . . . . . 76 5.2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5.2.3 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . 84 Stochastic control for uncertain contaminant source location . . 85 5.3.1 Problem Description . . . . . . . . . . . . . . . . . . . 86 5.3.2 Stochastic estimation problems . . . . . . . . . . . . . 86 5.3.3 Stochastic optimal control problems . . . . . . . . . . . 91 5.3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5.3.5 Remarks . . . . . . . . . . . . . . . . . . . . . . . . . 100 viii Conclusions and Future Work 102 6.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 Bibliography 106 A Finite Element Method 120 A.1 Solar components . . . . . . . . . . . . . . . . . . . . . . . . . 120 A.2 Finite Element Methods . . . . . . . . . . . . . . . . . . . . . . 123 A.2.1 Linear triangular element . . . . . . . . . . . . . . . . . 123 A.2.2 Elemental Matrices . . . . . . . . . . . . . . . . . . . . 125 B Optimization algorithm for control ix 127 Use white color here to extend the page to end Thesis summary Use white color here to extend the page to end This thesis presents an end-to-end measure-invert-control strategy for a stochastic problem with application to the management of water quality in a reservoir system. The strategy involves estimating uncertain contaminant source locations within a reservoir, followed by applying an optimal velocity field control to flush the contaminant out of the reservoir, while accounting for uncertainty such as wind velocity and measurement noise. This thesis first develops a finite element numerical simulation code for a 2D laterally averaged reservoir model. The numerical code is validated through comparisons to various benchmark problems. Numerical results show that the simulated hydrodynamic processes are in good agreement with theoretical and experimental data. The determination of the contaminant source location is posed as a Bayesian inference problem and solved using a Markov chain Monte Carlo (MCMC) method. Gaussian mixture models are used to approximately represent the posterior distribution of estimated source locations. The stochastic control problem then seeks an optimal velocity to flush the contaminant out of the reservoir. This control problem is solved using an adjoint method together with collocation over the space of uncertain parameters. For large-scale models, such as for reservoir applications, these computa- x [54] M. Kirby, J. P. Boris, and L. Sirovich. A proper orthogonal decomposition of a simulated supersonic shear layer. Int. J. Numerical Methods Fluids, 10:411–428, 1990. [55] K. Kunisch K and S. Volkwein. Galerkin proper orthogonal decomposition methods for a general equation in fluid dynamics. SIAM J. Numer. Anal., 40(2):492–515, 2002. [56] M. Gunzburger, J. S. Peterson, and J. N. Shadid. Reduced-ordr modeling of time-dependent PDEs with multiple parameters in the boundary data. Comput. Methods Appl. Mech. Engrg., 196:1030–1047, 2007. [57] K. Willcox. Unsteady flow sensing and estimation via the gappy proper orthogonal decomposition. Computers & Fluids, 35(2):208–226, 2006. [58] K. Kunisch and S. Volkwein. Control of the burgers equation by a reduced-order approach using proper orthogonal decomposition. Journal of Optimization Theory and Applications, 102:345–371, 1999. [59] S. S. Ravindran. Proper Orthogonal Decomposition in optimal control of fluids. Int. J. Numer. Meth. Fluids, 34:425–448, 1999. [60] A. J. Fletcher. Computational Galerkin Methods. Springer, New York, 1st edition, 1984. [61] W. R. Graham, J. Peraire, and K. Y. Tang. Optimal control of vortex shedding using low-order models. Part I: Open-loop model development. International Journal for Numerical Methods in Engineering, 44(7):945– 972, March 1999. 113 [62] W. R. Graham, J. Peraire, and K. Y. Tang. Optimal control of vortex shedding using low-order models. Part II: Model-based control. International Journal for Numerical Methods in Engineering, 44(7):973–990, March 1999. [63] B. R. Noack, M. Schlegel, M. Morzynski, and G. Tadmor. Galerkin Method for Nonlinear Dynamics, volume 528 of Reduced-Order Modelling for Flow Control, CISM Courses and Lectures, pages 111–149. Springer Vienna, 2011. [64] M. Gunzburger and J. Ming. Optimal control of stochastic flow over a backward-facing step using reduced-order modeling. SIAM J. Sci. Comput., 33(5):2641–2663, 2011. [65] M. Barrault, Y. Maday, N. C. Nguyen, and A. T. Patera. Empirical interpolation method: Application to efficient reduced-basis discretization of partial differential equations. Comptes Rendus Mathematique, 339(9):667–672, 2004. [66] M. A. Grepl, Y. Maday, N. C. Nguyen, and A. T. Patera. Effcients reduced-basis treatment of nonaffine and nonlinear partial differential equation. Mathematical Modelling and Numerical Analysis, 41(3):575– 605, 2007. [67] S. Chaturantabut and D. Sorensen. Discrete empirical interpolation for nonlinear model reduction. In Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on. 114 [68] S. Chaturantabut. Nonlinear Model Reduction via Discrete Empirical Interpolation. PhD dissertation, Rice University, Houston, Texas, 2011. [69] V. B. Nguyen. Computational simulation of detonation waves and model reduction for reacting flows. PhD dissertation, SMA, National University of Singapore, Singapore, 2011. [70] S. R. Karpik and G. D. Raithby. Laterally averaged hydrodynamics model for reservoir predictions. Journal of Hydraulic Engineering, 116(6):783– 798, 1990. [71] L. Bui and K. Mori. A two-dimensional numerical model of windinduced flow and water quality in closed water bodies. Paddy and Water Environment, 5(1):29–40, March 2007. [72] R. G. Kurup, D. P. Hamilton, and R. L. Phillips. Comparison of two 2-dimensional, laterally averaged hydrodynamic model applications to the Swan river estuary. Mathematics and Computers in Simulation, 51(6):627–638, February 2000. [73] D. R. Maidment. Handbook of Hydrology. McGraw-Hill, New York, 1993. [74] C. D. Wilcox. Turbulence Modeling for CFD. DCW Industries, La Caada, California, 1993. [75] L. Bengtsson. Conclusions about turbulent exchange coefficients from model studies. International Symposium Hydrology of lakes, Helsinki, pages 306–312. IAHS publication, 1973. 115 [76] R. Codina and J. Blasco. Stabilized finite element method for the transient Navier-Stokes equations based on a pressure gradient projection. Comput. Methods Appl. Mech. Engrg, 182:277–300, 2000. [77] O. C. Zienkiewicz and K. Morgan. Finite Elements and Approximation. Wiley, New York, 1983. [78] A. J. Chorin. Numerical solution of the Navier-Stokes equations. Math. Comp, 22:745–762, 1968. [79] D. P. Kouri. Optimization governed by stochastic partial differential equations. Master thesis, Rice University, Houston, Texas, 2010. [80] R. G. Ghanem and P. D. Spanos. Stochastic Finite Elements: A Spectral Approach. Springer - Verlag, New York, 1991. [81] U. Ghia, K. N. Ghia, and C. T. Shin. High-resolution for incompressible flow using the Navier-Stokes equations and the multigrid method. Journal of Computational Physics, 44:387–411, 1982. [82] J. Kim, S. J. Kline, and J. P. Johnston. Investigation of a reattaching turbulent shear layer flow over a backward-facing step. Journal of Fluids Engineering, 102(3):302–308, 1979. [83] C. G. Speziale and Tuan Ngo. Numerical solution of turbulent flow past a backward facing step using a nonlinear k–e model. International Journal of Engineering Science, 26(10):1099–1112, 1988. 116 [84] S. Thangam and N. Hur. A highly-resolved numerical study of turbulent separated flow past a backward-facing step. International Journal of Engineering Science, 29(5):607–615, 1991. [85] S. Karaa and J. Zhang. High order ADI method for solving unsteady convectiondiffusion problems. Journal of Computational Physics, 198(1):1– 9, 2004. [86] B. J. Noye and H. H. Tan. Finite difference methods for solving the two-dimensional advectiondiffusion equation. International Journal for Numerical Methods in Fluids, 9(1):75–98, 1989. [87] D. W. Peaceman and Jr. H. H. Rachford. The numerical solution of parabolic and elliptic differential equations. Journal of the Society for Industrial and Applied Mathematics, 3(1):28–41, 1955. [88] J. Crank and P. Nicolson. A practical method for numerical evaluation of solutions of partial differential equations of the heat-conduction type. Advances in Computational Mathematics, 6(1):207–226, 1996. [89] G. Hauke. An Introduction to Fluid Mechanics and Transport Phenomena, volume 86 of Fluid Mechanics and Its Applications. Springer, 2008. [90] K. Kunisch and L. Xie. Suboptimal feedback control of flow separation by POD Model Reduction, chapter 12, pages 233–249. Real-Time PDEConstrained Optimization. SIAM, 2007. [91] D. P. Bertsekas. Nonlinear Programming. Athena Scientific, second edition, 1999. 117 [92] C. W. Clenshaw and A. R. Curtis. A method for numerical integration on an automatic computer. Numerische Mathematik, 2:197–205, October 1960. [93] E. T. Jaynes. Information theory and statistical mechanics. Physical Review, 104(4):620–630, May 1957. [94] P. Br´emaud. Markov Chains, Gibbs Fields, Monte Carlo Simulation, and Queues. Springer-Verlag, New York, 1999. [95] C. M. Bishop. Pattern Recognition and Machine Learning - Information Science and Statistics. Springer, 2006. [96] Y. C. Ho and R. C. K. Lee. A Baysian approach to problems in stochastic estimation and control. Automatic Control, IEEE Transactions on, 9(4):333–339, 1964. [97] A. V. Oppenheim and G. C. Verghese. Chapter 8: Estimation with minimum mean square error, (class code 6.011). Lecture notes on Introduction to Communication, Control, and Signal Processing, MIT Open Course Ware, 2010. [98] J. Besag, P. Green, D. Higdon, and K. Mengersen. Bayesian computation and stochastic systems. Statistical Science, 10(1):3–41, 1995. [99] D. A. Siegel and T. D. Dickey. Variability of net longwave radiation over Eastern North Pacific ocean. Journal of Geophysical Reseach, 91:7657– 7666, June 1980. 118 [100] S. C. Chapra. Surface water-quality modeling. McGraw-Hill, New York, 1996. [101] J. R. Shewchuk. An introduction to the conjugate gradient method without the agonizing pain. Technical report CMU-CS-94-125, Carnegie Mellon University, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 1994. [102] L. Armijo. Minimization of functions having Lipschitz continuous first partial derivatives. Pacific Journal of Mathematics, 16(1):1–3, 1966. 119 Appendix A Finite Element Method A.1 Solar components We consider the solar component relationships as shown in Figure 2-1. The total net heat flux through the water surface is calculated by the net all-wave radiation [1], given by ∗ RN = RSN + RAN − RBR − RC − RL . (A.1) 1. The net solar shortwave radiation RSN = βRS (1 − α), (A.2) where RS is the incoming solar shortwave radiation, α ∈ (0, 1) is the water reflection coefficient, β = 0.65 is the fraction of solar shortwave radiation. The remaining fraction of the solar shortwave radiation (1 − β)RS is absorbed ex- 120 ponentially with depth as follows Rz = (1 − β)RS exp(−ηH), (A.3) where H is water depth and η = 0.5 is the extinction coefficient. 2. The down-welling longwave radiation: is expressed in terms of the StefanBoltzmann Law, for more details refers to [99], RAN = σεef f (273 + Ta )4 = σεcs Fcs (273 + Ta )4 . (A.4) Here Ta is air temperature, σ = 5.67 × 10−8 (W m−2 K −4 ) is Stefan-Boltzmann constant, εef f = εcs Fcs is referred to as the effective or apparent emissivity, Fcs ≥ is a cloud factor expressing the increase in clear-sky, εcs is the clear-sky atmospheric emissivity, εcs = (1 − )(1 + 0.17 ∗ C )Kf Ta2 , (A.5) where = 0.03 is the albedo for long wave radiation, Kf = 9.37 × 10−6 (K −2 ) a coefficient and ≤ C ≤ is cloud cover fraction. 3. The up-welling longwave radiation: follows the same formulation of the dowwelling, in which air temperature Ta is replaced by water surface temperature Ts , RBR = σεw (273 + Ts )4 , 121 (A.6) where the emissivity εw is fixed at 0.975. 4. The evaporative loss (latent heat flux) from the water is given as RL = f (Ua )(es − ea ), (A.7) where es and ea is the saturation vapor pressure above the water surface and the vapor pressure of air (hP a), which can be computed from [100], es = 4.596 exp ( ea = 17.27Ta ), 237.3 + Ta (A.8) es Rh , 100 (A.9) where Rh is relative humidity. The function of Ua is expressed as follows f (Ua ) = 7.6 × 10−4 × (9.2 + 0.46Ua2 ). (A.10) 5. The conduction heat loss (sensible heat flux) from the water RC = 0.47f (Ua )(Ts − Ta ). (A.11) Note that the dimension of all the radiation fluxes are expressed as energy per unit area (W m−2 ). 122 A.2 Finite Element Methods A.2.1 Linear triangular element Consider a linear triangular element with three nodal values φi = (u, w, p, c, T )i and nodal coordinate (x, z)i , with i = 1, 2, as shown in Figure A-1. The variable interpolation within the element is linear in x and z directions, as φ = α0 + α1 x + α2 z, (A.12) where αi are constants to be determined. The interpolation function (A.12) should represent the nodal variables at the three nodal points. Therefore, substituting x and z values at each nodal point gives       x1 z1   α0   φ1            1 x z α  = φ .   2 2             x3 z3 α2 φ3 (A.13) Inverting the matrix and rewriting equation (A.13), gives       α0   a1 a2 a3   φ1            α  =  b b b φ ,   2A               α2 c1 c2 c3 φ3 123 (A.14) where A is the area of the triangle and is given by the determinant    x1 z1       A = det  x2 z2  ,     x3 z3 (A.15) and a1 = x2 z3 − x3 z2 b1 = z2 − z3 c1 = x3 − x2 a2 = x3 z1 − x1 z3 b2 = z3 − z1 c2 = x1 − x3 (A.16) a3 = x1 z2 − x2 z1 b3 = z1 − z2 c3 = x2 − x1 . Substituting the coefficients into equation (A.12) and rearrange, we have Figure A-1: Linear triangle element. φ = H1 φ1 + H2 φ2 + H3 φ3 , or φ = Hφ(e) . (A.17) Here N are the shape functions, defined as H1 = (a 2A + b1 x + c1 z), H2 = (a 2A + b2 x + c2 z), H3 = (a 2A + b3 x + c3 z), 124 (A.18) These shape functions satisfy the conditions Hi (xj , zj ) = δij , (A.19) Hi = 1. (A.20) i=1 Here δij is is the Kronecker delta function. A.2.2 Elemental Matrices The finite element matrices are evaluated on each element as Me = HT HdD e , (A.21) De ∂H e dD , ∂x De ∂H e Gez = HT dD , ∂z De Gex = HT Le = De fxe = fze = Ce (u) = (A.22) (A.23) ∇HT ∇HdD e , (A.24) HT fx dD e , (A.25) HT fz dD e , (A.26) De De ∂H ∂H + Hwh dD e + ∂x ∂z De ∂H ∂H HT H uh + wh dD e , ∂x ∂z De HT Huh Ke (u) = De Ae (u) = ν(u)∇HT ∇HdD e , ∂H ∂H uh + wh dD e , ∂x ∂z ∂HT ∂HT Huh + Hwh dD e . ∂x ∂z HT De De (u) = De 125 (A.27) (A.28) (A.29) (A.30) Kec = fce = Cec (u) = De κ(θ k )∇HT ∇HdD e , (A.31) HT fc dD e , (A.32) De HT Huh De ∂H ∂H + Hwh dD e . ∂x ∂z 126 (A.33) Appendix B Optimization algorithm for control Algorithm is a general procedure to solve for deterministic control, stochastic control with deterministic source and stochastic control with uncertain source. For particular problem, we need to set the input parameter appropriately. For example, if the deterministic control is considered, we set P = 0, NG = 0; Θ = 0, etc. To solve the KKT system, the Crank-Nicolson method [88] is used to discretize the state, adjoint and optimality condition equations in time. The conjugate gradient method [101] is employed to solve the linearized system; the Armijo line-search [102] is used to ensure convergence. 1. Initial work 1a. Given P , D, NG , Θ, Φ, initial velocity u0 , tolerance ε. Set j = 1b. Given the FEM basis ϕl for l = 1, ., N, where N is the number of grid points 1c. Compute the matrices M, Cc (u) 127 1d. Compute collocation point {θk = (ξ, η)}Pk=1 and collocation weights {w k }Pk=1 . 2. Solve for the KKT system For j = : NG Use 2a. Compute vector F(t, φj ) = D f (x, t, φj )ϕi dx Use For k = : P Use for 2b. Compute input κ(x, t; Y) at each θ k Use for 2c. Compute K(t; θ k ) = D κ(x, t; Y)∇ϕi(x) · ∇ϕl (x)dx Use for 2d. Solve the state equations with input uj Use for 2e. Solve the adjoint equations Use for 2f. Store results Use end end 3. Compute the optimal control 3.a Compute the cost-functional Jˆ(uj ) and the gradient grad(uj ) 3.b If grad(uj ) < ǫ → stop. 3.c Perform Armijo line search • Set sj = −grad(uj ) • Set αj = then evaluate Jˆ(uj +αj sj ), and gtol = 10−4αj sTj grad(uj ) • While Jˆ(uj + αj sj ) > Jˆ(uj ) + gtol Set αj = αj /2 and evaluate Jˆ(uj + αj sj ). 3.d Set uj+1 = uj + αj sj , and j = j + 1. Go to step 2. 128 [...]... transport process where water velocities play a key role in the near field and wind induced water velocity is an important factor in the far field In this process, the in ows push the water towards and outflows pull/push the water out, while the wind induced flow exerts a drag on the water surface and causes floating objects to move in the wind direction Wind induced flow also causes the circulation of water, ... chemical and sediment loading as a point in ow source of contaminant and developed an optimal control model to determine the optimal pollutant loads at different in ux points along the course of a river in order to reduce the environmental damage costs In the study by Alvarez-Vazquez et al [29], the strategy consists of the injection of clean water from a reservoir at a nearby point into the river in order... lake and reservoirs in more detail, such as Ji [12], Martin [13], Orlob [14], and Rubin and Atkinson [15]) 1.2.2 Inverse problems The direct or forward problems compute the distribution of contaminant directly from given input information such as contaminant location, contaminant properties, fluid flow properties, boundary conditions, initial conditions, etc On the contrary, the inverse problems infer... stored water treated for 1 consumption, it is important to monitor, determine and remove any (suspected) contaminants as much as possible out of the water system Estimating and locating contaminant sources and then applying the control to flush them out of the water system are the rudimentary tasks of water quality management The tasks require knowledge of physics, hydrodynamics, data assimilation and. .. has built up many reservoirs from river systems to store water Rainwater, runoff water, etc., are collected and initially treated by a system of storm drains and storm sewers before entering a reservoir However, there can be other unexpected water sources that flow directly into the reservoir These unexpected sources may contain contaminant concentrations that cause pollution of the water body Hence... equations of velocity none Coefficients in the mode amplitude equations of contaminant none B The local width of the domain cp The specific heat of water c A contaminant concentration D Physical and computational domain none Fr Froude number none the forward model none he the local size of element none H The average depth of reservoir m A size of general ROM none Mu A size of ROM of velocity none Mc A size of. .. basins downstream As a result, reservoirs receive large water in ows from the surrounding watershed The flushing/flow rates are also rapid in order to balance water volume in reservoirs Thus, although there is large variation in water quality such as pollution loads entering reservoirs from in ows, reservoirs have the potential to flush these pollutants out rapidly This process is called the contaminant. .. Galerkin method for incompressible flow In this method, a set of nonlinear systems is approximated using a finite Galerkin expansion in term of global modes, obtained the evolution equation for the mode amplitudes, called the Galerkin system [60] In the context of optimal control problems, this approach improves the efficiency of computation by simplifying the full and complex optimality system, resulting in. .. parameters, and each single evaluation can be a computationally expensive undertaking 1.2.3 Optimal control for reservoir problems Optimal control can be used as a strategy to treat the polluted water in groundwater, rivers and reservoirs For example, Nicklow et al [25] applied the control on water discharge to minimize sediment scour and deposition in rivers and reservoirs, while Fontane et al [26] controlled... for simulating water quality in lakes and reservoirs are DYRESM (1981) [3] and CE-QUAL-W2 (1994) [4] These existing models have been used for simulation and validation for many studies and applications For example, Gu and Chung (2003) [5] studied the transport and fate of toxic chemicals in a stratified reservoir by modeling the toxic sub-model, then linked to CE-QUAL-W2 model using Microsoft Fortran . ESTIMATION AND CONTROL OF CONTAMINANT TRANSPORT IN WATER RESERVOIRS NGUYEN NGOC HIEN (B. Eng (Hons), Ho Chi Minh City University of Technology) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF. . 60 4.2.3 Numerical example for ROM of non-linear system . . . 62 5 Optimal control for contaminant transport 66 5.1 Deterministic control for contaminant transport . . . . . . . . . 67 5.1.1. management of water quality in a reservoir system. The strategy involves estimating uncertain contaminant source locations within a reservoir, followed by applying an optimal velocity field control